• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.164-169
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• 2003

In this paper, we propose the integrated Bayesian network framework to reconstruct genetic regulatory networks from genome expression data. The proposed model overcomes the dimensionality problem of multivariate analysis by building coherent sub-networks from confined gene clusters and combining these networks via intermediary points. Gene Shaving algorithm is used to cluster genes that share a common function or co-regulation. Retrieved clusters incorporate prior biological knowledge such as Gene Ontology, pathway, and protein protein interaction information for extracting other related genes. With these extended gene list, system builds genetic sub-networks using Bayesian network with MDL score and Sparse Candidate algorithm. Identifying functional modules of genes is done by not only microarray data itself but also well-proved biological knowledge. This integrated approach can improve there liability of a network in that false relations due to the lack of data can be reduced. Another advantage is the decreased computational complexity by constrained gene sets. To evaluate the proposed system, S. Cerevisiae cell cycle data [1] is applied. The result analysis presents new hypotheses about novel genetic interactions as well as typical relationships known by previous researches [2].

• Genomics & Informatics
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• 제4권1호
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• pp.33-39
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• 2006

Alternative splicing (AS) is an important mechanism of producing transcriptome diversity and microarray techniques are being used increasingly to monitor the splice variants. There exist three types of microarrays interrogating AS events-junction, exon, and tiling arrays. Junction probes have the advantage of monitoring the splice site directly. Johnson et al., performed a genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays (Science 302:2141-2144, 2003), which monitored splicing at every known exon-exon junctions for more than 10,000 multi-exon human genes in 52 tissues and cell lines. Here, we describe an algorithm to deduce the relative concentration of isoforms from the junction array data. Non-negative Matrix Factorization (NMF) is applied to obtain the transcript structure inferred from the expression data. Then we choose the transcript models consistent with the ECgene model of alternative splicing which is based on mRNA and EST alignment. The probe-transcript matrix is constructed using the NMF-consistent ECgene transcripts, and the isoform abundance is deduced from the non-negative least squares (NNLS) fitting of experimental data. Our method can be easily extended to other types of microarrays with exon or junction probes.

• Toxicological Research
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• 제30권4호
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• pp.283-289
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• 2014

Tea flavonoids and polyphenols are well known for their extraordinary antioxidant activity which is considered important for anti-aging processes in animals. This study evaluated the anti-wrinkle effects of three different kinds of tea (Camellia sinensis) water extracts (CSWEs) including green, white, and black teas using a photoaged hairless mouse model. Data showed that the CSWE-treatment greatly improved skin conditions of mice suffering from UVB-induced photoaging, based on the parameters including the skin erythema index, moisture capacity, and transepidermal water loss. In addition, the wrinkle measurement and image analysis of skin replicas indicated that CSWEs remarkably inhibited wrinkle formation. In histological examination, the CSWE-treated mice exhibited diminished epidermal thickness and increased collagen and elastic fiber content, key signatures for skin restoration. Furthermore, the reduced expression of MMP-3, a collagen-degradative enzyme, was observed in the skin of CSWE-treated animals. Interestingly, comparative data between green, white, and black tea indicated that the anti-wrinkle activity of white tea and black tea is equally greater than that of green tea. Taken together, these data clearly demonstrated that CSWEs could be used as an effective anti-wrinkle agent in photoaged animal skin, implying their extended uses in therapeutics.

• BMB Reports
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• 제38권2호
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• pp.243-247
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• 2005

Dishevelled (Dvl) is a positive regulator of the canonical Wnt signaling pathway, which regulates the levels of $\beta$-catenin. The $\beta$-catenin oncoprotein depends upon the association of Dvl and Axin proteins through their DIX domains, and its accumulation directs the expression of specific developmental-related genes at the nucleus. Here, the $^1H$, $^{13}C$, and $^{15}N$ resonances of the human Dishevelled 2 DIX domain are assigned using heteronuclear nuclear magnetic resonance (NMR) spectroscopy. In addition, helical and extended elements are identified based on the NMR data. The results establish a structural context for characterizing the actin and phospholipid interactions and binding sites of this novel domain, and provide insights into its role in protein localization to stress fibers and cytoplasmic vesicles during Wnt signaling.

• 한국CDE학회논문집
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• 제11권6호
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• pp.393-402
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• 2006

The purpose of this study is to develop mechanisms of nD model-based design by the combination of 2D drawing standards and 3D building models from the current 2D and text-based design. The aim of this study can be archived by defining the 2D model extension definitions for the IFC model development and harmonizing existing 2D standards. The paper examines 1) 3D Representation of Building Element and Building Services element, and 2D Model extension of IFC2X.2, 2) Basic development of additional 2D element that should be added to IFC model, and 3) mapping method between current 2D standard and IFC2.X2. Following this approach, the interoperability problem between 3D model and 2D drawing can be solved and finally an extended data model could be developed.

• 감성과학
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• 제24권2호
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• pp.75-80
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• 2021

Do happy applicants achieve more? Although it is well established that happiness predicts desirable work-related outcomes, previous findings were primarily obtained in social settings. In this study, we extended the scope of the "happiness premium" effect to the artificial intelligence (AI) context. Specifically, we examined whether an applicant's happiness signal captured using an AI system effectively predicts his/her objective performance. Data from 3,609 job applicants showed that verbally expressed happiness (frequency of positive words) during an AI interview predicts cognitive task scores, and this tendency was more pronounced among women than men. However, facially expressed happiness (frequency of smiling) recorded using AI could not predict the performance. Thus, when AI is involved in a hiring process, verbal rather than the facial cues of happiness provide a more valid marker for applicants' hiring chances.

• The Korean Journal of Physiology and Pharmacology
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• 제25권5호
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• pp.395-401
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• 2021

Extended inflammation and cytokine production pathogenically contribute to a number of inflammatory disorders. Formosanin C (FC) is the major diosgenin saponin found in herb Paris formosana Hayata (Liliaceae), which has been shown to exert anti-cancer and immunomodulatory functions. In this study, we aimed to investigate anti-inflammatory activity of FC and the underlying molecular mechanism. RAW264.7 macrophages were stimulated with lipopolysaccharide (LPS) or pretreated with FC prior to being stimulated with LPS. Thereafter, the macrophages were subjected to analysis of the expression levels of pro-inflammatory mediators, including nitric oxide (NO), prostaglandin E₂ (PGE), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and IL-6, as well as two relevant enzymes, inducible nitric oxide synthase (iNOS), and cyclooxygenase-2 (COX-2). The analysis revealed that FC administration blunted LPS-induced production of NO and PGE in a dose-dependent manner, while the expression of iNOS and COX-2 at both mRNA and protein levels was inhibited in LPS-stimulated macrophages pre-treated with FC. Moreover, LPS stimulation upregulated mRNA expression and medium release of TNF-α, IL-1β, and IL-6, whereas this effect was blocked upon FC pre-administration. Mechanistic studies showed that inhibitory effects of FC on LPS-induced inflammation were associated with a downregulation of IκB kinase, IκB, and p65/NF-κB pathway. Taken together, these data suggest that FC possesses an inflammation-suppressing activity, thus being a potential agent for the treatment of inflammation-associated disorders.

• 한국융합학회논문지
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• 제11권3호
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• pp.1-7
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• 2020

본 논문은 불완전한 데이터를 처리하기 위해 2가지의 서로 다른 기법과 이를 학습하는 알고리즘을 소개한다. 첫째방법은 손실변수가 가질 수 있는 균등한 확률로 손실값을 할당하여 불완전한 데이터를 처리하고, SVM 알고리즘으로 이 데이터를 학습하는 것이다. 이 기법은 임의의 변수에 손실 값의 빈도가 높을수록 엔트로피가 높도록 하여 이 변수가 결정트리에서 선택되지 않도록 하는 것이다. 이 방법은 손실 변수에 남아있는 정보를 모두 무시하고 새로운 값을 할당한다는 특징이 있다. 이에 반해 새로운 방법은 손실 값을 제외하고 남아있는 정보로 엔트로피 확률을 구하고 이를 손실 변수의 추정 값으로 사용하는 것이다. 즉, 불완전한 학습데이터로부터 소실되지 않은 많은 정보들을 이용해 소실된 일부 정보를 복구하고 딥러닝을 이용해 학습한다. 이 2가지 방법은 학습데이터에서 차례로 변수 하나를 선택하고, 이 변수에 손실된 데이터의 비율을 달리하면서 서로 다른 측정값들의 결과들과 반복적으로 비교함으로써 성능을 측정한다.

• 한국융합학회논문지
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• 제10권2호
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• pp.1-6
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• 2019

제안 모델은 소실 데이터를 포함하는 불완전한 데이터에서 정보의 손실을 최소화할 수 있도록 개발되었다. 이를 위한 과정은 우선 데이터 확장기법을 이용하여 손실 정보를 보상하도록 학습 데이터를 변환한다. 이 변환 과정에서 데이터의 속성값은 원-핫 인코딩으로 이진 또는 확률값으로 채워진다. 다음 이 변환 데이터는 딥러닝 모델에 입력되는데, 이때 각 속성의 카디너리티에 따라 엔트리 수가 일정하지 않게 된다. 그리고 각 속성의 엔트리 값들을 각각의 입력 노드에 할당하고 학습을 진행한다. 이점이 기존 학습 모델과의 차이점으로, 임의의 속성값이 입력층에서 여러 개의 노드로 분산되는 특이한 구조를 가진다. 제안 모델의 학습 성능을 평가하기 위해, 소실 데이터를 대상으로 다양한 실험을 수행하여 성능 면에서 우수함을 보인다. 제안 모델은 유비쿼터스 환경에서 손실을 최소화하기 위한 알고리즘으로 유용하게 사용될 것으로 본다.

• Genomics & Informatics
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• 제10권2호
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• pp.69-73
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• 2012

The explosive development of genomics technologies including microarrays and next generation sequencing (NGS) has provided comprehensive maps of cancer genomes, including the expression of mRNAs and microRNAs, DNA copy numbers, sequence variations, and epigenetic changes. These genome-wide profiles of the genetic aberrations could reveal the candidates for diagnostic and/or prognostic biomarkers as well as mechanistic insights into tumor development and progression. Recent efforts to establish the huge cancer genome compendium and integrative omics analyses, so-called "integromics", have extended our understanding on the cancer genome, showing its daunting complexity and heterogeneity. However, the challenges of the structured integration, sharing, and interpretation of the big omics data still remain to be resolved. Here, we review several issues raised in cancer omics data analysis, including NGS, focusing particularly on the study design and analysis strategies. This might be helpful to understand the current trends and strategies of the rapidly evolving cancer genomics research.